The goal of gas turbine performance diagnosis is to accurately detect, isolate and assess performance changes, system malfunctions and instrumentation problems. Among a number of other techniques, Gas Path Analysis (GPA) is a well established framework for estimating shifts in performance from the knowledge of measured parameters, such as power, engine speeds, temperatures, pressures or fuel flow, taken along the gas path of the turbine. Discernable shifts in these measured parameters provide the requisite information for determining the underlying shift in engine operation from a presumed reference, nominal or initial state, i.e. the degradation symptoms. GPA allows engine performance deterioration to be identified in terms of a degradation of independent parameters or system states such as thermodynamic efficiencies, flow capacities and inlet/outlet filter areas. In a subsequent diagnosis step, these degradation symptoms can be analysed, from which a maintenance action schedule to ensure economic and safe operation is deduced, or from which the remaining life of the major components is predicted. The origin of a fault affecting a given component of the gas turbine can be of various nature, such as, for example, a contamination of compressor blades, erosion of turbine blades or corrosion of machine parts. Conversely, different faults often create similar observable effects or degradation symptoms.
According to the patent application EP-A 1 233 165, a degradation state of an industrial gas turbine is determined with the aid of measurements during operation of the gas turbine. The proposed Gas Path Analysis uses a mathematical simulation or an analytical performance model of a gas turbine engine based upon component characteristics of the engine in question, such as compressor and turbine maps and including thermodynamic and aerodynamic operating behaviour of the gas turbine such as the laws of conservation of energy and mass. The model permits values of measurable output variables or dependent variables to be determined from input variables such as, for example, air inlet temperature and pressure, as well as from assumptions concerning state or independent parameters corresponding to non-measurable degradation symptoms. The output variables are, for example, pressures, mass flows and temperatures at various points in the gas path of the gas turbine, a fuel mass flow, a rotational speed and an output mechanical power. Deviations of state parameters, such as efficiencies or flow capacities, from a reference value represent symptoms of a degradation of a component of the gas turbine.
In particular, a deviation of a measured deteriorated performance y′ from an iterated base-line performance is multiplied with a fault coefficient matrix, derived from a theoretical relationship between the independent parameters x and the dependent parameters y of the form y=F(x), to yield an improved estimation of the exact solution x′=F−1(y′) and the next iterated base-line. In other words, a repeated application of linear Gas Path Analysis based on iterated base-line performance via the Newton-Raphson technique is used to approach the exact solution, i.e. the independent vector x′ corresponding to the measured deteriorated performance y′.
In the patent application EP-A 1 418 481 a framework for aero gas turbine diagnosis is proposed which distinguishes between rapid deterioration due to singular system fault events and gradual deterioration due to damage accumulation of all engine components. A measurement Δ vector, comprising deviations from a reference of some gas path parameter data such as rotor speed, temperatures and pressures, reflects the effects of a multitude of possible engine/sensor system fault occurrences as well as random measurement noise. From this measurement Δ vector at a discrete time k, a total fault vector xk comprising the engine system and sensor faults as the current states is estimated within a Module Performance Analysis (MPA). Apart from a reference to statistical tests or neural networks, the estimation method is not detailed. If a rapid deterioration event is in progress, single fault or root cause isolation is performed, based on the change ΔΔk in the measurement Δ vector w. r. t. the previous measurement at time k−1. Otherwise, multiple fault isolation is performed to yield an updated error vector, based on the cumulative share ZkMFI of the measurement Δ vector assigned to gradual deterioration.
The different components of a gas turbine (GT), which consist mainly of the inlet nozzle, the compressor, the combustion chamber, the turbine, the cooling flow, and the outlet, all contribute—to a different extent—to the degradation of GT performance. Because a small deviation from new-and-clean conditions already results in a significant loss of performance, the problem of identifying and localizing symptoms of the overall degradation is of crucial importance.